Fix the tests (#1894)

* make tests faster * skip tests that always fail on tf-nightly * fix tests * fix tests * remove nightly tests * remove nightly
keras-team · Oct 2, 2023 · ec4ebd1 · ec4ebd1
1 parent 04525a5
commit ec4ebd1
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Showing 3 changed files with 48 additions and 58 deletions.
diff --git a/.github/workflows/nightly.yml b/.github/workflows/nightly.yml
diff --git a/autokeras/integration_tests/task_api_test.py b/autokeras/integration_tests/task_api_test.py
@@ -20,39 +20,50 @@
 import autokeras as ak
 from autokeras import test_utils
 
+NUM_INSTANCES = 3
+BATCH_SIZE = 2
+
 
 def test_image_classifier(tmp_path):
-    train_x = test_utils.generate_data(num_instances=320, shape=(32, 32))
+    train_x = test_utils.generate_data(
+        num_instances=NUM_INSTANCES, shape=(32, 32)
+    )
     train_y = test_utils.generate_one_hot_labels(
-        num_instances=320, num_classes=10
+        num_instances=NUM_INSTANCES, num_classes=10
     )
     clf = ak.ImageClassifier(
         directory=tmp_path,
         max_trials=2,
         seed=test_utils.SEED,
         distribution_strategy=tf.distribute.MirroredStrategy(),
     )
-    clf.fit(train_x, train_y, epochs=1, validation_split=0.2)
+    clf.fit(
+        train_x, train_y, epochs=1, validation_split=0.2, batch_size=BATCH_SIZE
+    )
     keras_model = clf.export_model()
     clf.evaluate(train_x, train_y)
     assert clf.predict(train_x).shape == (len(train_x), 10)
     assert isinstance(keras_model, keras.Model)
 
 
 def test_image_regressor(tmp_path):
-    train_x = test_utils.generate_data(num_instances=320, shape=(32, 32, 3))
-    train_y = test_utils.generate_data(num_instances=320, shape=(1,))
+    train_x = test_utils.generate_data(
+        num_instances=NUM_INSTANCES, shape=(32, 32, 3)
+    )
+    train_y = test_utils.generate_data(num_instances=NUM_INSTANCES, shape=(1,))
     clf = ak.ImageRegressor(
         directory=tmp_path, max_trials=2, seed=test_utils.SEED
     )
-    clf.fit(train_x, train_y, epochs=1, validation_split=0.2)
+    clf.fit(
+        train_x, train_y, epochs=1, validation_split=0.2, batch_size=BATCH_SIZE
+    )
     clf.export_model()
     assert clf.predict(train_x).shape == (len(train_x), 1)
 
 
 def test_text_classifier(tmp_path):
-    train_x = test_utils.generate_text_data(num_instances=320)
-    train_y = np.random.randint(0, 2, 320)
+    train_x = test_utils.generate_text_data(num_instances=NUM_INSTANCES)
+    train_y = np.array([0, 1] * ((NUM_INSTANCES + 1) // 2))[:NUM_INSTANCES]
     test_x = train_x
     test_y = train_y
     clf = ak.TextClassifier(
@@ -67,29 +78,35 @@ def test_text_classifier(tmp_path):
         train_y,
         epochs=2,
         validation_data=(test_x, test_y),
-        batch_size=6,
+        batch_size=BATCH_SIZE,
     )
     clf.export_model()
     assert clf.predict(test_x).shape == (len(test_x), 1)
     assert clf.tuner._get_best_trial_epochs() <= 2
 
 
 def test_text_regressor(tmp_path):
-    train_x = test_utils.generate_text_data(num_instances=300)
+    train_x = test_utils.generate_text_data(num_instances=NUM_INSTANCES)
     test_x = train_x
-    train_y = test_utils.generate_data(num_instances=300, shape=(1,))
+    train_y = test_utils.generate_data(num_instances=NUM_INSTANCES, shape=(1,))
     test_y = train_y
     clf = ak.TextRegressor(
         directory=tmp_path, max_trials=2, seed=test_utils.SEED
     )
-    clf.fit(train_x, train_y, epochs=1, validation_data=(test_x, test_y))
+    clf.fit(
+        train_x,
+        train_y,
+        epochs=1,
+        validation_data=(test_x, test_y),
+        batch_size=BATCH_SIZE,
+    )
     clf.export_model()
     assert clf.predict(test_x).shape == (len(test_x), 1)
 
 
 def test_structured_data_regressor(tmp_path):
-    num_data = 500
-    num_train = 400
+    num_data = NUM_INSTANCES * 2
+    num_train = NUM_INSTANCES
     data = (
         pd.read_csv(test_utils.TRAIN_CSV_PATH).to_numpy().astype(str)[:num_data]
     )
@@ -99,14 +116,20 @@ def test_structured_data_regressor(tmp_path):
     clf = ak.StructuredDataRegressor(
         directory=tmp_path, max_trials=2, seed=test_utils.SEED
     )
-    clf.fit(x_train, y_train, epochs=11, validation_data=(x_train, y_train))
+    clf.fit(
+        x_train,
+        y_train,
+        epochs=11,
+        validation_data=(x_train, y_train),
+        batch_size=BATCH_SIZE,
+    )
     clf.export_model()
     assert clf.predict(x_test).shape == (len(y_test), 1)
 
 
 def test_structured_data_classifier(tmp_path):
-    num_data = 500
-    num_train = 400
+    num_data = NUM_INSTANCES * 2
+    num_train = NUM_INSTANCES
     data = (
         pd.read_csv(test_utils.TRAIN_CSV_PATH).to_numpy().astype(str)[:num_data]
     )
@@ -118,7 +141,13 @@ def test_structured_data_classifier(tmp_path):
     clf = ak.StructuredDataClassifier(
         directory=tmp_path, max_trials=1, seed=test_utils.SEED
     )
-    clf.fit(x_train, y_train, epochs=2, validation_data=(x_train, y_train))
+    clf.fit(
+        x_train,
+        y_train,
+        epochs=2,
+        validation_data=(x_train, y_train),
+        batch_size=BATCH_SIZE,
+    )
     clf.export_model()
     assert clf.predict(x_test).shape == (len(y_test), 3)
 

diff --git a/autokeras/test_utils.py b/autokeras/test_utils.py
@@ -69,7 +69,7 @@ def generate_data(num_instances=100, shape=(32, 32, 3), dtype="np"):
 def generate_one_hot_labels(num_instances=100, num_classes=10, dtype="np"):
     np.random.seed(SEED)
     labels = np.random.randint(num_classes, size=num_instances)
-    data = keras.utils.to_categorical(labels)
+    data = keras.utils.to_categorical(labels, num_classes=num_classes)
     if dtype == "np":
         return data
     if dtype == "dataset":